Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, machine learning algorithms have gained popularity in the field of economic forecasting. This study aims to predict the Indian Gross Domestic Product (GDP) using advanced machine learning algorithms. To achieve this, we collected data from various sources, including time series analysis and inflation rate. We analyzed the data using linear regression and polynomial regression techniques to determine which method produced the most accurate results. Our results showed that the polynomial regression model outperformed the linear regression model in terms of accuracy. The polynomial regression model was better able to capture the non-linear relationships between the independent variables and the dependent variable (GDP). Specifically, our findings showed that the polynomial regression model was able to predict the Indian GDP with an accuracy of 91%, compared to 87% for the linear regression model. This study highlights the importance of using advanced machine learning algorithms in economic forecasting. We found that the use of high-quality data sets and advanced techniques such as polynomial regression can significantly improve the accuracy of economic forecasts. Our findings have several implications for policymakers and businesses. Accurate predictions of economic indicators such as GDP can help businesses make informed decisions about investment and growth strategies, while policymakers can use these predictions to develop effective economic policies. Overall, our study provides valuable insights into the use of machine learning algorithms in predicting Indian GDP. Our findings demonstrate the effectiveness of polynomial regression in capturing non-linear relationships and improving the accuracy of economic forecasts. This study can be used as a reference for future research in this area and emphasizes the need for high-quality data sets and advanced machine-learning techniques in economic forecasting.